By Dr. Michael H. Azarian
While electronics provide tools for entertainment and leisure, electronics have also become critical to our security and safety. As such, managing downtime and maintenance is critical. With available compute power, expanding sensor technology, and machine learning, the ability to anticipate degradation and prevent in-use failure can be achieved. Prognostics and health management (PHM) combines monitoring and analysis of environmental, operational, and performance-related parameters to assess the health of a system and predict its remaining useful life.
This talk will describe data-driven and physics-of-failure-based PHM strategies employed at the Center for Advanced Life Cycle Engineering (CALCE), as well as the fusion of these approaches. One problem for complex systems, which is particularly common for electronics, is how to perform diagnostics on systems that contain a large number of potential failure sites and that are not adequately equipped with embedded sensors. Some potential solutions to this problem will be illustrated using examples from research in this area.